How to deal with “I don’t know”

What companies don’t know can hurt them when it comes to launching digital initiatives. Here are some trail markers to manage the uncertainty.

As I work with my clients and observe companies, there is a huge and growing desire to turn digital capabilities into growth. The KPIs aren’t around productivity and “lean” (though that’s still important, of course); they focus on revenue, customer satisfaction, and growth.

The problem is that for many digital initiatives that look to unlock new profit pools and markets, companies don’t know what the upside is because there are so many variables and unknowns: quality of design, how it’s marketed, and even sheer luck, e.g., the right celebrity gets hold of the offer or service and makes it popular. That “not knowing” can really hamper people and make it virtually impossible to take action. So what happens is companies get stuck in cycles of endless meetings, miscommunication, frustration, and inertia.

But there are a few ways to deal with this that I've found to be very effective. It really boils down to being systematic about getting your hands around the unknowns. It’s helpful to run scenarios, for example, that quantify what the growth could be, what the conditions are for that, what you believe to be true, then how to manage costs in the most prudent way so that you spend carefully before take up.

That naturally leads to developing use cases and personas. You’ll need to develop at least four to eight personas. Then for each of those, what are the top three to five use cases that create value or address costs and frustration.

These need to be explicit and detailed – what social media customers are using, how they use their mobile phones. It’s so important to understand the context, and then you have to play out the use cases, and identify the implications. For example, if I want to send a text message to a customer with a discount on an item that they’d be interested in at a store that’s nearby, the implication is that I need to know who has mobile apps and where they are relative to where the store is.

That then implies the work that needs to be done, such as pooling and organizing all the various offers and developing algorithms that match an offer to a specific person. Then there needs to be another data call to make sure the item is in stock. At that point, you can get a pretty concrete view of what you can and cannot do and what you need to do. You can start to identify the range of data needed to make those use cases generate value for both the company and the customer. With that in place, you can prioritize around value.

You also have to be comfortable with hypotheses. Data doesn’t just naturally tell you things; you need to know what you’re looking for. For example, one hypothesis is that I can motivate customers to come back to a store if they’re within 200 yards of the store, as opposed to a customer who has never been in the store. That’s a pretty basic hypothesis, but it then helps direct you to the kind of data and intelligence you need to focus on. And if that hypothesis is important and you don’t have the data, you can then go get the data, e.g., by providing opportunities for customers to opt in to get an app and then provide incentives for them to use it. It’s a matter of thinking through what you think is likely to happen, and then looking for the data to act on it.

In many cases, people know much more than they think they know. They just need to organize how they're thinking about the various scenarios, personas, and use cases in a systematic way.